Statistics and Data Science in Imaging (2024).

Abstract

Recent advances in medical imaging technologies have led to the proliferation of multimodal quantitative imaging data in cancer. Quantitative medical imaging data—numerical representations derived from radiology and pathology—enable precise characterization of tumor biology to assess progression, therapy response, and prognosis. However, analytical challenges arise due to high dimensionality, structural correlations, and heterogeneity. This review summarizes state‑of‑the‑art statistical methods for quantitative imaging—including topological, functional, and shape analyses; spatial process models; and modern ML—highlighting clinical applications in oncology and open problems for future research.